Comprehensive View Embedding Learning for Single-Cell Multimodal Integration

Motivation: Advances in single-cell measurement techniques provide rich multimodal data, which helps us to explore the life state of cells more deeply. However, multimodal integration, or, learning joint embeddings from multimodal data remains a current challenge. The difficulty in integrating unpaired single-cell multimodal data is that different modalities have different feature spaces, which easily leads to information loss in joint embedding. And few existing methods have fully exploited and fused the information in single-cell multimodal data. Result: In this study, we propose CoVEL, a deep learning method for unsupervised integration of single-cell multimodal data. CoVEL learns single-cell representations from a comprehensive view, including regulatory relationships between modalities, fine-grained representations of cells, and relationships between different cells. The comprehensive view embedding enables CoVEL to remove the gap between modalities while protecting biological heterogeneity. Experimental results on multiple public datasets show that CoVEL is accurate and robust to single-cell multimodal integration. Data availability: https://github.com/shapsider/scintegration.

Authors

• Zhenchao Tang
• Jiehui Huang School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University
• Guanxing Chen School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University
• Calvin Yu-Chian Chen School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University AI for Science (AI4S) - Preferred Program, Peking University Shenzhen Graduate School School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School Department of Medical Research, China Medical University Hospital Department of Bioinformatics and Medical Engineering, Asia University

Keywords

• APP: Natural Sciences
• APP: Other Applications
• ML: Applications